Catsandmeowreuploads/claude-opus-4.6-10000x-reupload

--- license: mit --- # Notice This is a reupload of claude opus 4.6 10000x, incase it ever gets deleted. If you request this dataset must be deleted, please say so in the community tab. This is a high-fidelity reasoning dataset synthesized using Claude Opus 4.6. The dataset is designed to capture the model's internal "Chain of Thought" and reasoning traces, specifically focusing on mathematical accuracy and structured logical deduction. The dataset is intended for Supervised Fine-Tuning (SFT) and Distillation, allowing smaller open-source models to inherit the sophisticated reasoning patterns of Claude Opus 4.6. Dataset Description This collection combines high-difficulty math problems (GSM8K, MATH) with general-purpose logic puzzles and multi-step instructions. Each row includes a hidden reasoning trace where the model "thinks" through the problem before providing the final answer. By exposing the fine-tuned model to these internal monologues, the resulting model learns process-oriented thinking rather than just pattern-matching for answers. Why Simple Logic & Math Improves Reasoning Fine-tuning on "Simple Logic and Math" serves as a cognitive foundation for LLMs for several reasons: Rule Adherence: Math requires strict following of operations. Training on these paths reduces "hallucinations" in non-math tasks. Step-by-Step Verification: These examples force the model to break down complex problems into smaller, verifiable units. Cross-Domain Generalization: The ability to solve a "simple" logic puzzle translates into better coding, legal analysis, and structured writing, as all these tasks rely on the same underlying cognitive architecture of premise → deduction → conclusion. Stats ## Teacher Model: [Claude Opus 4.6](https://www.anthropic.com/news/claude-opus-4-6) **Total Cost: $ 872.00 (USD)** **Total Tokens (Input + Output): 27.2 M** **Format: JSONL (Conversational with Reasoning Traces)** **Primary Categories: Mathematics, Symbolic Logic, General Purpose Problem Solving** ### Usage This dataset is optimized for fine-tuning models such as Qwen3.5 27b,25b a3b, 9b, 4b, 2b, 0.8b to increase their performance on benchmarks like BigBench Hard and GSM8K without increasing their parameter count.--- 许可证：MIT许可证 --- # 注意事项 本仓库为Claude Opus 4.6 10000x数据集的重新上传，以防原数据集被删除。若您需申请删除本数据集，请在社区板块留言告知。 本数据集为由Claude Opus 4.6生成的高保真推理数据集，旨在捕获该模型的内部「思维链（Chain of Thought）」与推理轨迹，重点关注数学准确性与结构化逻辑演绎。 本数据集适用于监督微调（Supervised Fine-Tuning, SFT）与知识蒸馏，可让更小的开源模型继承Claude Opus 4.6的复杂推理模式。 ## 数据集说明 本合集整合了高难度数学题（涵盖GSM8K、MATH数据集）、通用逻辑谜题与多步指令任务。每条数据均包含一段隐藏的推理轨迹，即模型给出最终答案前的思考过程。通过让待微调模型学习这类内部独白，最终模型将习得面向过程的思维方式，而非仅依赖模式匹配获取答案。 ## 为何基础逻辑与数学训练可提升推理能力 针对「基础逻辑与数学」进行微调，能够为大语言模型（Large Language Model, LLM）构建认知基础，原因如下： 1. **规则遵循性**：数学解题需要严格遵循运算规则，基于此类样本训练可降低模型在非数学任务中产生幻觉的概率。 2. **分步验证能力**：这类示例会迫使模型将复杂问题拆解为可验证的小型单元。 3. **跨领域泛化性**：解决「基础」逻辑谜题的能力，可迁移至编程、法律分析与结构化写作等任务中，因为所有这类任务均依托「前提→演绎→结论」这一通用认知架构。 ## 数据集统计 ### 教师模型：[Claude Opus 4.6](https://www.anthropic.com/news/claude-opus-4-6) **总成本：872.00美元（USD）** **总Token数（输入+输出）：2720万** **数据格式：JSONL（含推理轨迹的对话式格式）** **核心分类：数学、符号逻辑、通用问题求解** ### 使用说明 本数据集专为微调Qwen3.5 27B、25B、A3B、9B、4B、2B、0.8B等模型优化，可在不增加参数量的前提下，提升模型在BigBench Hard与GSM8K等基准测试中的表现。